Project Description:
A decrease in air quality presents a significant hazard to the sustainability of environmental conditions in the modern world. Its significance in shaping health outcomes and the quality of life in urban areas is projected to escalate over time. Many factors, encompassing anthropogenic emissions and natural phenomena, are recognised as primary influencers contributing to the escalation of air pollution levels. Human health is particularly threatened by high amounts of pollution caused by weather events or disasters. However, these extreme events are difficult to predict using machine learning techniques due to their rapid onset and rarity. Our research aims to provide an analysis and predictive model of extreme air quality events.
This project is part of MBIE Taiao Programme https://taiao.ai/.
Keywords: Continual Learning, Extreme Events, Air Quality, Replay Learning, Data Streams